ML in energy sector revolutionizing the energy sector machine learning applications for efficiency, sustainability and predictive analytics

The increasing global energy demand, coupled with the need for sustainability, has necessitated innovative solutions in energy management. This study explores an application of ML techniques to revolutionize the energy sector, emphasizing efficiency, sustainability, and predictive analytics. This study evaluates a performance of proposed ML models in optimizing energy efficiency and predictive analytics for renewable energy applications. Using real-time sensor data encompassing energy consumption, weather conditions, equipment malfunctions, and grid statistics, the dataset was preprocessed and analyzed with proposed models: RF, Neural Networks, GB, SVM, and KNN. These models were assessed using metrics such as accuracy, training time, scalability, interpretability, and energy impact. Among the proposed models, Neural Networks achieved the highest accuracy, 92% and energy impact, 30%, while Random Forest offered a balanced trade-off between accuracy (89%), scalability, and interpretability. The outcomes underscore a potential of the proposed ML models in advancing energy systems, highlighting Neural Networks for optimization and Random Forest for real-time applications. Future work aims to address computational limitations and expand model adaptability for diverse energy scenarios.